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Handling Missing Data in Health Economics and Outcomes Research (HEOR): A Systematic Review and Practical Recommendations

Author

Listed:
  • Kumar Mukherjee

    (Philadelphia College of Osteopathic Medicine)

  • Necdet B. Gunsoy

    (Abbvie Ltd.)

  • Rita M. Kristy

    (Astellas Pharma)

  • Joseph C. Cappelleri

    (Pfizer Inc.)

  • Jessica Roydhouse

    (University of Tasmania)

  • Judith J. Stephenson

    (Carelon Research)

  • David J. Vanness

    (Pennsylvania State University)

  • Sujith Ramachandran

    (University of Mississippi)

  • Nneka C. Onwudiwe

    (Pharmaceutical Economics Consultants of America)

  • Sri Ram Pentakota

    (Rutgers New Jersey Medical School)

  • Helene Karcher

    (Novartis Pharma AG)

  • Gian Luca Di Tanna

    (University of Applied Sciences and Arts of Southern Switzerland)

Abstract

Background Missing data in costs and/or health outcomes and in confounding variables can create bias in the inference of health economics and outcomes research studies, which in turn can lead to inappropriate policies. Most of the literature focuses on handling missing data in randomized controlled trials, which are not necessarily always the data used in health economics and outcomes research. Objectives We aimed to provide an overview on missing data issues and how to address incomplete data and report the findings of a systematic literature review of methods used to deal with missing data in health economics and outcomes research studies that focused on cost, utility, and patient-reported outcomes. Methods A systematic search of papers published in English language until the end of the year 2020 was carried out in PubMed. Studies using statistical methods to handle missing data for analyses of cost, utility, or patient-reported outcome data were included, as were reviews and guidance papers on handling missing data for those outcomes. The data extraction was conducted with a focus on the context of the study, the type of missing data, and the methods used to tackle missing data. Results From 1433 identified records, 40 papers were included. Thirteen studies were economic evaluations. Thirty studies used multiple imputation with 17 studies using multiple imputation by chained equation, while 15 studies used a complete-case analysis. Seventeen studies addressed missing cost data and 23 studies dealt with missing outcome data. Eleven studies reported a single method while 20 studies used multiple methods to address missing data. Conclusions Several health economics and outcomes research studies did not offer a justification of their approach of handling missing data and some used only a single method without a sensitivity analysis. This systematic literature review highlights the importance of considering the missingness mechanism and including sensitivity analyses when planning, analyzing, and reporting health economics and outcomes research studies.

Suggested Citation

  • Kumar Mukherjee & Necdet B. Gunsoy & Rita M. Kristy & Joseph C. Cappelleri & Jessica Roydhouse & Judith J. Stephenson & David J. Vanness & Sujith Ramachandran & Nneka C. Onwudiwe & Sri Ram Pentakota &, 2023. "Handling Missing Data in Health Economics and Outcomes Research (HEOR): A Systematic Review and Practical Recommendations," PharmacoEconomics, Springer, vol. 41(12), pages 1589-1601, December.
    • Handle: RePEc:spr:pharme:v:41:y:2023:i:12:d:10.1007_s40273-023-01297-0
    DOI: 10.1007/s40273-023-01297-0
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    References listed on IDEAS

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    1. Feng-Chang Lin & Jianwen Cai & Jason P. Fine & Huichuan J. Lai, 2013. "Nonparametric estimation of the mean function for recurrent event data with missing event category," Biometrika, Biometrika Trust, vol. 100(3), pages 727-740.
    2. Alastair Canaway & Emma Frew & Emma Lancashire & Miranda Pallan & Karla Hemming & Peymane Adab & on behalf of the WAVES trial investigators, 2019. "Economic evaluation of a childhood obesity prevention programme for children: Results from the WAVES cluster randomised controlled trial conducted in schools," PLOS ONE, Public Library of Science, vol. 14(7), pages 1-14, July.
    3. Paul C. Lambert & Lucinda J. Billingham & Nicola J. Cooper & Alex J. Sutton & Keith R. Abrams, 2008. "Estimating the cost‐effectiveness of an intervention in a clinical trial when partial cost information is available: a Bayesian approach," Health Economics, John Wiley & Sons, Ltd., vol. 17(1), pages 67-81, January.
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